When I use run_FT.sh, only [task_name] and [EX_NAME_SUFFIX] need to input. I change the model_name_or_path to where the bert-base-uncased is.
Firstly, an error appeared:
ValueError: Some specified arguments are not used by the HfArgumentParser: ['--data_dir', './datasets/RTE']
Checking the log, I find that the model will find datasets in cache, so I delete the argument 'data_dir'.
However,
during pre-finetuning, the accuracy for dev is very small.
In the evaluation output file, it is only 0.47, and I found the sparsity is 0.666.
Task: rte
Model path: /home/ykw/cofi/out-test/RTE/RTE_test_RTE/
Model size: 28385280
Sparsity: 0.6659999999999999
accuracy: 0.4729
seconds/example: 0.00093
Why did the pre-finetune process prune the model? It even don't need to input a sparsity number. And the accuracy is really smaller than yours (0.70).
When I use run_FT.sh, only [task_name] and [EX_NAME_SUFFIX] need to input. I change the model_name_or_path to where the bert-base-uncased is.
Firstly, an error appeared: ValueError: Some specified arguments are not used by the HfArgumentParser: ['--data_dir', './datasets/RTE'] Checking the log, I find that the model will find datasets in cache, so I delete the argument 'data_dir'.
However, during pre-finetuning, the accuracy for dev is very small. In the evaluation output file, it is only 0.47, and I found the sparsity is 0.666.
Task: rte Model path: /home/ykw/cofi/out-test/RTE/RTE_test_RTE/ Model size: 28385280 Sparsity: 0.6659999999999999 accuracy: 0.4729 seconds/example: 0.00093
Why did the pre-finetune process prune the model? It even don't need to input a sparsity number. And the accuracy is really smaller than yours (0.70).